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arXiv 2608.05665physics.med-ph

虚拟听力诊所(VHC)—— 用于听力研究和听力保健的模块化在线平台

The Virtual Hearing Clinic (VHC)- a modular online platform for hearing research and hearing health care

Lena Schell-Majoor, Kim M. Rullmann, Tobias Bruns, Theresa Jansen, Volker Hohmann, Hendrik Kayser, Birger Kollmeier

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中文总结 AI 辅助

该研究介绍了模块化在线平台虚拟听力诊所(VHC),通过对比GraBr程序获取的20名受试者阈值与临床听力计参考值,证实其可用于移动听力保健及听力学研究,能降低听力保健障碍并收集大型数据集。

中文摘要 AI 辅助

目的:本文旨在介绍虚拟听力诊所(VHC)的概念,概述其当前状态,并通过获取听力阈值的诊断模块数据示例说明其可行性。设计:描述了VHC的架构,并概述了已在相应研究中开发和测试的功能模块。作为功能示例,呈现了一项实验的数据:使用分级响应 bracketing(GraBr)程序,通过VHC从20名听力损失受试者处获取听力阈值,并与临床 audiometer(听力计)获得的参考阈值进行比较。结果:所有频率下基于VHC的听力阈值中位数与参考值无显著差异,VHC的数值为57.4 dB SPL,参考值为55.5 dB SPL;250 Hz至4 kHz的频率依赖性阈值形态也非常相似,6 kHz的结果显示出更大差异。结论:VHC适用于移动听力保健应用,并可为听力学研究收集数据;通过提供时间和地点上的灵活性,它可降低听力保健的障碍,并能够收集数据驱动型听力学发展所需的大型数据集。

英文摘要

Objective: The aim is to introduce the concept of the Virtual Hearing Clinic (VHC), give an overview of the current status and exemplify its feasibility with data from a diagnostics module obtaining hearing thresholds. Design: The architecture of the VHC is described and an overview of functional modules that have been developed and tested in respective studies is given. As a functional example data from an experiment is presented. Hearing thresholds were obtained from 20 subjects with hearing loss with the VHC using the Graded Response Bracketing (GraBr) procedure and compared to reference thresholds obtained with a clinical audiometer. Results: Median VHC-based hearing thresholds over all frequencies did not differ significantly from the reference with values of 57.4 dB SPL (VHC) and 55.5 dB SPL (reference). The shape of the frequency-dependent thresholds was also found to be very similar for 250 Hz to 4 kHz. Results for 6 kHz showed larger differences. Conclusion: The VHC is suitable as a mobile hearing health application and to collect data for audiological research. By offering flexibility in time and location it can lower barriers for hearing health care and enable collecting large datasets that are needed for the advancement of data-driven audiology.

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